mistral-7b-qlora-remote-decorator_selfsupervised.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Fine-tune Mistral-7B with QLoRA and SageMaker remote decorator\n",
    "\n",
    "## Self-supervised fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "In this demo notebook, we demonstrate how to fine-tune the Mistral-7B model using QLoRA, Hugging Face PEFT, and bitsandbytes.\n",
    "\n",
    "We are using SageMaker remote decorator for runinng the fine-tuning job on Amazon SageMaker Training job\n",
    "---\n",
    "\n",
    "JupyterLab Instance Type: ml.t3.medium\n",
    "\n",
    "PyTorch version: 3.11\n",
    "\n",
    "Fine-Tuning:\n",
    "* Instance Type: ml.g5.12xlarge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the required libriaries, including the Hugging Face libraries, and restart the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:07:37.160628Z",
     "start_time": "2024-01-26T09:07:24.214358Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -r requirements.txt --upgrade\n",
    "%pip install -q -U python-dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:08:34.593905Z",
     "start_time": "2024-01-26T09:07:40.813986Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -q -U langchain==0.1.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Setup Configuration file path\n",
    "\n",
    "We are setting the directory in which the config.yaml file resides so that remote decorator can make use of the settings through [SageMaker Defaults](https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk).\n",
    "\n",
    "This notebook is using the Hugging Face container for the `us-east-1` region. Make sure you are using the right image for your AWS region, otherwise edit [config.yaml](./config.yaml). Container Images are available [here](https://github.com/aws/deep-learning-containers/blob/master/available_images.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:10:25.268548Z",
     "start_time": "2024-01-26T09:10:25.266384Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "import os\n",
    "\n",
    "# Use .env in case of hidden variables\n",
    "load_dotenv()\n",
    "\n",
    "# Set path to config file\n",
    "os.environ[\"SAGEMAKER_USER_CONFIG_OVERRIDE\"] = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize and upload the dataset\n",
    "\n",
    "Read train dataset in a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:10:30.198991Z",
     "start_time": "2024-01-26T09:10:26.881694Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.document_loaders import WebBaseLoader\n",
    "\n",
    "loader = WebBaseLoader([\n",
    "    \"https://aws.amazon.com/bedrock/\",\n",
    "    \"https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html\",\n",
    "    \"https://aws.amazon.com/blogs/aws/preview-enable-foundation-models-to-complete-tasks-with-agents-for-amazon-bedrock/\",\n",
    "    \"https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html\",\n",
    "    \"https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html\",\n",
    "\n",
    "])\n",
    "\n",
    "data = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:10:38.812254Z",
     "start_time": "2024-01-26T09:10:36.505800Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from datasets import Dataset\n",
    "\n",
    "def strip_spaces(doc):\n",
    "    return {\"text\": doc.page_content.replace(\"  \", \"\")}\n",
    "\n",
    "stripped_data = list(map(strip_spaces, data))\n",
    "\n",
    "train_dataset = Dataset.from_list(stripped_data)\n",
    "\n",
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! huggingface-cli login --token <HF_TOKEN>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:10:51.943040Z",
     "start_time": "2024-01-26T09:10:41.527153Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "model_id = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating chunks and tokenizing the inputs for making it usable by the LLM. For additional details, please refer to the blog [Leveraging qLoRA for Fine-Tuning of Task-Fine-Tuned Models Without Catastrophic Forgetting: A Case Study with LLaMA2(-chat)](https://medium.com/towards-data-science/leveraging-qlora-for-fine-tuning-of-task-fine-tuned-models-without-catastrophic-forgetting-d9bcd594cff4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:11:28.748430Z",
     "start_time": "2024-01-26T09:11:28.742863Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "from functools import partial\n",
    "\n",
    "remainder = {\"input_ids\": [], \"attention_mask\": [], \"token_type_ids\": []}\n",
    "\n",
    "def chunk(sample, chunk_length=2048):\n",
    "    # define global remainder variable to save remainder from batches to use in next batch\n",
    "    global remainder\n",
    "    # Concatenate all texts and add remainder from previous batch\n",
    "    concatenated_examples = {k: list(chain(*sample[k])) for k in sample.keys()}\n",
    "    concatenated_examples = {k: remainder[k] + concatenated_examples[k] for k in concatenated_examples.keys()}\n",
    "    # get total number of tokens for batch\n",
    "    batch_total_length = len(concatenated_examples[list(sample.keys())[0]])\n",
    "\n",
    "    # get max number of chunks for batch\n",
    "    if batch_total_length >= chunk_length:\n",
    "        batch_chunk_length = (batch_total_length // chunk_length) * chunk_length\n",
    "\n",
    "    # Split by chunks of max_len.\n",
    "    result = {\n",
    "        k: [t[i : i + chunk_length] for i in range(0, batch_chunk_length, chunk_length)]\n",
    "        for k, t in concatenated_examples.items()\n",
    "    }\n",
    "    # add remainder to global variable for next batch\n",
    "    remainder = {k: concatenated_examples[k][batch_chunk_length:] for k in concatenated_examples.keys()}\n",
    "    # prepare labels\n",
    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "To train our model, we need to convert our inputs (text) to token IDs. This is done by a Hugging Face Transformers Tokenizer. In addition to QLoRA, we will use bitsanbytes 4-bit precision to quantize out frozen LLM to 4-bit and attach LoRA adapters on it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:10:38.829988Z",
     "start_time": "2024-01-26T09:10:38.809336Z"
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    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "tags": []
   },
   "outputs": [],
   "source": [
    "def print_trainable_parameters(model):\n",
    "    \"\"\"\n",
    "    Prints the number of trainable parameters in the model.\n",
    "    \"\"\"\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_params += param.numel()\n",
    "    print(\n",
    "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utility method for finding the target modules and update the necessary matrices. Visit [this](https://github.com/artidoro/qlora/blob/main/qlora.py) link for additional info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T09:11:31.665117Z",
     "start_time": "2024-01-26T09:11:31.116541Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import bitsandbytes as bnb\n",
    "\n",
    "def find_all_linear_names(hf_model):\n",
    "    lora_module_names = set()\n",
    "    for name, module in hf_model.named_modules():\n",
    "        if isinstance(module, bnb.nn.Linear4bit):\n",
    "            names = name.split(\".\")\n",
    "            lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
    "\n",
    "    if \"lm_head\" in lora_module_names:  # needed for 16-bit\n",
    "        lora_module_names.remove(\"lm_head\")\n",
    "    return list(lora_module_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the train function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:25:06.721135Z",
     "start_time": "2023-11-20T18:25:04.369468Z"
    },
    "collapsed": false,
    "jupyter": {
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   },
   "outputs": [],
   "source": [
    "from accelerate import Accelerator\n",
    "import datetime\n",
    "from huggingface_hub import snapshot_download\n",
    "import os\n",
    "from peft import AutoPeftModelForCausalLM, LoraConfig, get_peft_model, prepare_model_for_kbit_training\n",
    "from sagemaker.remote_function import remote\n",
    "import torch\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n",
    "import transformers\n",
    "\n",
    "# Start training\n",
    "@remote(\n",
    "    keep_alive_period_in_seconds=0,\n",
    "    volume_size=100,\n",
    "    job_name_prefix=f\"train-{model_id.split('/')[-1].replace('.', '-')}\"\n",
    ")\n",
    "def train_fn(\n",
    "        model_name,\n",
    "        train_ds,\n",
    "        test_ds=None,\n",
    "        lora_r=8,\n",
    "        lora_alpha=16,\n",
    "        lora_dropout=0.1,\n",
    "        per_device_train_batch_size=8,\n",
    "        per_device_eval_batch_size=8,\n",
    "        gradient_accumulation_steps=1,\n",
    "        learning_rate=2e-4,\n",
    "        num_train_epochs=1,\n",
    "        fsdp=\"\",\n",
    "        fsdp_config=None,\n",
    "        gradient_checkpointing=False,\n",
    "        merge_weights=False,\n",
    "        chunk_size=2048,\n",
    "        seed=42,\n",
    "        token=None\n",
    "):\n",
    "    def init_distributed():\n",
    "        # Initialize the process group\n",
    "        torch.distributed.init_process_group(backend=\"nccl\", timeout=datetime.timedelta(seconds=5400))  # Use \"gloo\" backend for CPU\n",
    "        local_rank = int(os.environ[\"LOCAL_RANK\"])\n",
    "        torch.cuda.set_device(local_rank)\n",
    "\n",
    "        return local_rank\n",
    "\n",
    "    # Call this function at the beginning of your script\n",
    "    local_rank = init_distributed()\n",
    "\n",
    "    if torch.cuda.device_count() > 1:\n",
    "        # Call this function at the beginning of your script\n",
    "        local_rank = init_distributed()\n",
    "\n",
    "        # Now you can use distributed functionalities\n",
    "        torch.distributed.barrier(device_ids=[local_rank])\n",
    "\n",
    "    set_seed(seed)\n",
    "\n",
    "    accelerator = Accelerator()\n",
    "\n",
    "    if token is not None:\n",
    "        os.environ.update({\"HF_TOKEN\": token})\n",
    "        accelerator.wait_for_everyone()\n",
    "\n",
    "    # Speed up model download\n",
    "    with accelerator.main_process_first():\n",
    "        if accelerator.is_main_process:\n",
    "            print(\"Downloading model \", model_name)\n",
    "\n",
    "            os.makedirs(\"/tmp/tmp_folder\", exist_ok=True)\n",
    "\n",
    "            snapshot_download(repo_id=model_name, local_dir=\"/tmp/tmp_folder\")\n",
    "\n",
    "            print(f\"Downloading model {model_name} under /tmp/tmp_folder\")\n",
    "\n",
    "    model_name = \"/tmp/tmp_folder\"\n",
    "\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "\n",
    "    # Set Tokenizer pad Token\n",
    "    tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "    with accelerator.main_process_first():\n",
    "        # tokenize and chunk dataset\n",
    "        lm_train_dataset = train_ds.map(\n",
    "            lambda sample: tokenizer(sample[\"text\"]), batched=True, remove_columns=list(train_ds.features)\n",
    "        ).map(\n",
    "            partial(chunk, chunk_length=chunk_size),\n",
    "            batched=True,\n",
    "        )\n",
    "    \n",
    "        if test_ds is not None:\n",
    "            lm_test_dataset = test_ds.map(\n",
    "                lambda sample: tokenizer(sample[\"text\"]), batched=True, remove_columns=list(test_ds.features)\n",
    "            ).map(\n",
    "                partial(chunk, chunk_length=chunk_size),\n",
    "                batched=True,\n",
    "            )\n",
    "\n",
    "            print(f\"Total number of test samples: {len(lm_test_dataset)}\")\n",
    "        else:\n",
    "            lm_test_dataset = None\n",
    "\n",
    "    torch_dtype = torch.bfloat16\n",
    "\n",
    "    # Defining additional configs for FSDP\n",
    "    if fsdp != \"\" and fsdp_config is not None:\n",
    "        bnb_config_params = {\n",
    "            \"bnb_4bit_quant_storage\": torch_dtype\n",
    "        }\n",
    "\n",
    "        model_configs = {\n",
    "            \"torch_dtype\": torch_dtype\n",
    "        }\n",
    "\n",
    "        fsdp_configurations = {\n",
    "            \"fsdp\": fsdp,\n",
    "            \"fsdp_config\": fsdp_config,\n",
    "            \"gradient_checkpointing_kwargs\": {\n",
    "                \"use_reentrant\": False\n",
    "            },\n",
    "            \"tf32\": True\n",
    "        }\n",
    "    else:\n",
    "        bnb_config_params = dict()\n",
    "        model_configs = dict()\n",
    "        fsdp_configurations = dict()\n",
    "\n",
    "    bnb_config = BitsAndBytesConfig(\n",
    "        load_in_4bit=True,\n",
    "        bnb_4bit_use_double_quant=True,\n",
    "        bnb_4bit_quant_type=\"nf4\",\n",
    "        bnb_4bit_compute_dtype=torch_dtype,\n",
    "        **bnb_config_params\n",
    "    )\n",
    "\n",
    "    model = AutoModelForCausalLM.from_pretrained(\n",
    "        model_name,\n",
    "        trust_remote_code=True,\n",
    "        quantization_config=bnb_config,\n",
    "        attn_implementation=\"flash_attention_2\",\n",
    "        use_cache=not gradient_checkpointing,\n",
    "        cache_dir=\"/tmp/.cache\",\n",
    "        **model_configs\n",
    "    )\n",
    "\n",
    "    if fsdp == \"\" and fsdp_config is None:\n",
    "        model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=gradient_checkpointing)\n",
    "\n",
    "    if gradient_checkpointing:\n",
    "        model.gradient_checkpointing_enable()\n",
    "\n",
    "    config = LoraConfig(\n",
    "        r=lora_r,\n",
    "        lora_alpha=lora_alpha,\n",
    "        target_modules=\"all-linear\",\n",
    "        lora_dropout=lora_dropout,\n",
    "        bias=\"none\",\n",
    "        task_type=\"CAUSAL_LM\"\n",
    "    )\n",
    "\n",
    "    model = get_peft_model(model, config)\n",
    "    print_trainable_parameters(model)\n",
    "\n",
    "    trainer = transformers.Trainer(\n",
    "        model=model,\n",
    "        train_dataset=lm_train_dataset,\n",
    "        eval_dataset=lm_test_dataset if lm_test_dataset is not None else None,\n",
    "        args=transformers.TrainingArguments(\n",
    "            per_device_train_batch_size=per_device_train_batch_size,\n",
    "            per_device_eval_batch_size=per_device_eval_batch_size,\n",
    "            gradient_accumulation_steps=gradient_accumulation_steps,\n",
    "            gradient_checkpointing=gradient_checkpointing,\n",
    "            logging_strategy=\"steps\",\n",
    "            logging_steps=1,\n",
    "            log_on_each_node=False,\n",
    "            num_train_epochs=num_train_epochs,\n",
    "            learning_rate=learning_rate,\n",
    "            bf16=True,\n",
    "            ddp_find_unused_parameters=False,\n",
    "            save_strategy=\"no\",\n",
    "            output_dir=\"outputs\",\n",
    "            **fsdp_configurations\n",
    "        ),\n",
    "        data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
    "    )\n",
    "\n",
    "    trainer.train()\n",
    "\n",
    "    if trainer.is_fsdp_enabled:\n",
    "        trainer.accelerator.state.fsdp_plugin.set_state_dict_type(\"FULL_STATE_DICT\")\n",
    "\n",
    "    if merge_weights:\n",
    "        output_dir = \"/tmp/model\"\n",
    "\n",
    "        # merge adapter weights with base model and save\n",
    "        # save int 4 model\n",
    "        trainer.model.save_pretrained(output_dir, safe_serialization=False)\n",
    "\n",
    "        if accelerator.is_main_process:\n",
    "            # clear memory\n",
    "            del model\n",
    "            del trainer\n",
    "\n",
    "            torch.cuda.empty_cache()\n",
    "\n",
    "            # load PEFT model\n",
    "            model = AutoPeftModelForCausalLM.from_pretrained(\n",
    "                output_dir,\n",
    "                torch_dtype=torch.float16,\n",
    "                low_cpu_mem_usage=True,\n",
    "                trust_remote_code=True,\n",
    "            )\n",
    "\n",
    "            # Merge LoRA and base model and save\n",
    "            model = model.merge_and_unload()\n",
    "            model.save_pretrained(\n",
    "                \"/opt/ml/model\", safe_serialization=True, max_shard_size=\"2GB\"\n",
    "            )\n",
    "    else:\n",
    "        trainer.model.save_pretrained(\"/opt/ml/model\", safe_serialization=True)\n",
    "\n",
    "    if accelerator.is_main_process:\n",
    "        tokenizer.save_pretrained(\"/opt/ml/model\")\n",
    "\n",
    "    accelerator.wait_for_everyone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-14T21:04:05.465078Z",
     "start_time": "2023-11-14T20:43:26.848983Z"
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    "collapsed": false,
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   "outputs": [],
   "source": [
    "train_fn(\n",
    "    model_id,\n",
    "    train_ds=train_dataset,\n",
    "    per_device_train_batch_size=2,\n",
    "    per_device_eval_batch_size=1,\n",
    "    gradient_accumulation_steps=2,\n",
    "    gradient_checkpointing=True,\n",
    "    chunk_size=2048,\n",
    "    num_train_epochs=3,\n",
    "    merge_weights=True,\n",
    "    token=os.environ.get(\"HF_TOKEN\", None) # Change None to your HF_TOKEN in case you don't use .env\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Deploy Fine-Tuned model\n",
    "\n",
    "Note: Run `train_fn` with `merge_weights=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:41:08.515277Z",
     "start_time": "2023-11-20T18:41:08.503555Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import sagemaker\n",
    "from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker_session = sagemaker.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:41:09.025472Z",
     "start_time": "2023-11-20T18:41:09.021475Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_id = \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
    "\n",
    "bucket_name = sagemaker_session.default_bucket()\n",
    "default_prefix = sagemaker_session.default_bucket_prefix\n",
    "job_prefix = f\"train-{model_id.split('/')[-1].replace('.', '-')}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_last_job_name(job_name_prefix):\n",
    "    import boto3\n",
    "    sagemaker_client = boto3.client('sagemaker')\n",
    "    \n",
    "    search_response = sagemaker_client.search(\n",
    "        Resource='TrainingJob',\n",
    "        SearchExpression={\n",
    "            'Filters': [\n",
    "                {\n",
    "                    'Name': 'TrainingJobName',\n",
    "                    'Operator': 'Contains',\n",
    "                    'Value': job_name_prefix\n",
    "                },\n",
    "                {\n",
    "                    'Name': 'TrainingJobStatus',\n",
    "                    'Operator': 'Equals',\n",
    "                    'Value': \"Completed\"\n",
    "                }\n",
    "            ]\n",
    "        },\n",
    "        SortBy='CreationTime',\n",
    "        SortOrder='Descending',\n",
    "        MaxResults=1)\n",
    "    \n",
    "    return search_response['Results'][0]['TrainingJob']['TrainingJobName']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_name = get_last_job_name(job_prefix)\n",
    "\n",
    "job_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Inference configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:41:10.492877Z",
     "start_time": "2023-11-20T18:41:10.488495Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "instance_count = 1\n",
    "instance_type = \"ml.g5.12xlarge\"\n",
    "number_of_gpu = 4\n",
    "health_check_timeout = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:41:11.162811Z",
     "start_time": "2023-11-20T18:41:10.787936Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "image_uri = get_huggingface_llm_image_uri(\n",
    "    \"huggingface\",\n",
    "    version=\"1.4\"\n",
    ")\n",
    "\n",
    "image_uri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:41:12.433399Z",
     "start_time": "2023-11-20T18:41:11.963091Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if default_prefix:\n",
    "    model_data = f\"s3://{bucket_name}/{default_prefix}/{job_name}/{job_name}/output/model.tar.gz\"\n",
    "else:\n",
    "    model_data = f\"s3://{bucket_name}/{job_name}/{job_name}/output/model.tar.gz\"\n",
    "\n",
    "model = HuggingFaceModel(\n",
    "    image_uri=image_uri,\n",
    "    model_data=model_data,\n",
    "    env={\n",
    "        'HF_MODEL_ID': \"/opt/ml/model\", # path to where sagemaker stores the model\n",
    "        'SM_NUM_GPUS': json.dumps(number_of_gpu), # Number of GPU used per replica\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:45:49.265298Z",
     "start_time": "2023-11-20T18:41:14.621743Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictor = model.deploy(\n",
    "    initial_instance_count=instance_count,\n",
    "    instance_type=instance_type,\n",
    "    container_startup_health_check_timeout=health_check_timeout,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:48:12.482198Z",
     "start_time": "2023-11-20T18:48:12.471432Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "base_prompt = f\"\"\"\n",
    "<s>[INST]\n",
    "{{question}} \n",
    "[/INST]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:48:49.471611Z",
     "start_time": "2023-11-20T18:48:44.127852Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "prompt = base_prompt.format(question=\"Which are the Foundation Models available in Amazon Bedrock?\")\n",
    "\n",
    "predictor.predict({\n",
    "\t\"inputs\": prompt,\n",
    "    \"parameters\": {\n",
    "        \"n_predict\": -1,\n",
    "        \"temperature\": 0.2,\n",
    "        \"top_p\": 0.9\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "#### Delete Endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-20T18:48:55.153276Z",
     "start_time": "2023-11-20T18:48:54.165351Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictor.delete_model()\n",
    "predictor.delete_endpoint(delete_endpoint_config=True)"
   ]
  }
 ],
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   {
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    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g5.12xlarge",
    "vcpuNum": 48
   },
   {
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    "_isFastLaunch": false,
    "category": "Accelerated computing",
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